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Gabrielsson, Patrick
Publications (3 of 3) Show all publications
Gabrielsson, P., Johansson, U. & König, R. (2014). Co-Evolving Online High-Frequency Trading Strategies Using Grammatical Evolution. Paper presented at IEEE Conference on Computational Intelligence for Financial Engineering & Economics, 27-28 March, 2014, London, UK. Paper presented at IEEE Conference on Computational Intelligence for Financial Engineering & Economics, 27-28 March, 2014, London, UK. IEEE
Open this publication in new window or tab >>Co-Evolving Online High-Frequency Trading Strategies Using Grammatical Evolution
2014 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Numerous sophisticated algorithms exist for discovering reoccurring patterns in financial time series. However, the most accurate techniques available produce opaque models, from which it is impossible to discern the rationale behind trading decisions. It is therefore desirable to sacrifice some degree of accuracy for transparency. One fairly recent evolutionary computational technology that creates transparent models, using a user-specified grammar, is grammatical evolution (GE). In this paper, we explore the possibility of evolving transparent entry- and exit trading strategies for the E-mini S&P 500 index futures market in a high-frequency trading environment using grammatical evolution. We compare the performance of models incorporating risk into their calculations with models that do not. Our empirical results suggest that profitable, risk-averse, transparent trading strategies for the E-mini S&P 500 can be obtained using grammatical evolution together with technical indicators.

Place, publisher, year, edition, pages
IEEE, 2014
Keywords
Grammatical evolution, High-frequency trading, Machine learning, Data mining
National Category
Computer Sciences Computer and Information Sciences
Identifiers
urn:nbn:se:hb:diva-7321 (URN)10.1109/CIFEr.2014.6924111 (DOI)2320/14713 (Local ID)2320/14713 (Archive number)2320/14713 (OAI)
Conference
IEEE Conference on Computational Intelligence for Financial Engineering & Economics, 27-28 March, 2014, London, UK
Note
Best paper award.Available from: 2015-12-22 Created: 2015-12-22 Last updated: 2018-01-10
Gabrielsson, P., König, R. & Johansson, U. (2013). Evolving Hierarchical Temporal Memory-Based Trading Models. Paper presented at Applications of Evolutionary Computation. Paper presented at Applications of Evolutionary Computation. Springer-Verlag
Open this publication in new window or tab >>Evolving Hierarchical Temporal Memory-Based Trading Models
2013 (English)Conference paper, Published paper (Refereed)
Abstract [en]

We explore the possibility of using the genetic algorithm to optimize trading models based on the Hierarchical Temporal Memory (HTM) machine learning technology. Technical indicators, derived from intraday tick data for the E-mini S&P 500 futures market (ES), were used as feature vectors to the HTM models. All models were configured as binary classifiers, using a simple buy-and-hold trading strategy, and followed a supervised training scheme. The data set was partitioned into multiple folds to enable a modified cross validation scheme. Artificial Neural Networks (ANNs) were used to benchmark HTM performance. The results show that the genetic algorithm succeeded in finding predictive models with good performance and generalization ability. The HTM models outperformed the neural network models on the chosen data set and both technologies yielded profitable results with above average accuracy.

Place, publisher, year, edition, pages
Springer-Verlag, 2013
Series
Lecture Notes in Computer Science ; 7835
Keywords
Algorithmic Trading, Hierarchical Temporal Memory, Data mining, Machine Learning
National Category
Computer Sciences Computer and Information Sciences
Identifiers
urn:nbn:se:hb:diva-7056 (URN)10.1007/978-3-642-37192-9_22 (DOI)2320/12921 (Local ID)2320/12921 (Archive number)2320/12921 (OAI)
Conference
Applications of Evolutionary Computation
Available from: 2015-12-22 Created: 2015-12-22 Last updated: 2018-01-10
Gabrielsson, P., König, R. & Johansson, U. (2012). Hierarchical Temporal Memory-based algorithmic trading of financial markets. Paper presented at Computational Intelligence for Financial Engineering & Economics (CIFEr), New York, NY, 2012. Paper presented at Computational Intelligence for Financial Engineering & Economics (CIFEr), New York, NY, 2012. IEEE
Open this publication in new window or tab >>Hierarchical Temporal Memory-based algorithmic trading of financial markets
2012 (English)Conference paper, Published paper (Refereed)
Abstract [en]

This paper explores the possibility of using the Hierarchical Temporal Memory (HTM) machine learning technology to create a profitable software agent for trading financial markets. Technical indicators, derived from intraday tick data for the E-mini S&P 500 futures market (ES), were used as features vectors to the HTM models. All models were configured as binary classifiers, using a simple buy-and-hold trading strategy, and followed a supervised training scheme. The data set was divided into a training set, a validation set and three test sets; bearish, bullish and horizontal. The best performing model on the validation set was tested on the three test sets. Artificial Neural Networks (ANNs) were subjected to the same data sets in order to benchmark HTM performance. The results suggest that the HTM technology can be used together with a feature vector of technical indicators to create a profitable trading algorithm for financial markets. Results also suggest that HTM performance is, at the very least, comparable to commonly applied neural network models.

Place, publisher, year, edition, pages
IEEE, 2012
Keywords
Machine learning, Data mining, Algorithmic trading
National Category
Computer Sciences Computer and Information Sciences
Research subject
Bussiness and IT
Identifiers
urn:nbn:se:hb:diva-6846 (URN)10.1109/CIFEr.2012.6327784 (DOI)000310365100022 ()2320/11579 (Local ID)978-1-4673-1802-0 (ISBN)2320/11579 (Archive number)2320/11579 (OAI)
Conference
Computational Intelligence for Financial Engineering & Economics (CIFEr), New York, NY, 2012
Available from: 2015-12-22 Created: 2015-12-22 Last updated: 2018-01-10
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